Applying Big Data in Higher Education: A Case Study of Teacher-Focused Learning Analytics

2020 ◽  
pp. 341-348
Author(s):  
Benson K. H. Hung
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Learning Analytics

scholarly journals Combining event- and variable-centred approaches to institution-facing learning analytics at the unit of study level

2017 ◽  
Vol 34 (1) ◽  
pp. 63-78 ◽  
Author(s):  
Nick Kelly ◽  
Maximiliano Montenegro ◽  
Carlos Gonzalez ◽  
Paula Clasing ◽  
Augusto Sandoval ◽  
...  
Keyword(s):  
Big Data ◽  
Learning Analytics ◽  
Process Mining ◽  
Approaches To Learning ◽  
Time Frame ◽  
Data Set ◽  
Content Type ◽  
Modelling Techniques ◽  
Level Of Understanding

Purpose The purpose of this paper is to demonstrate the utility of combining event-centred and variable-centred approaches when analysing big data for higher education institutions. It uses a large, university-wide data set to demonstrate the methodology for this analysis by using the case study method. It presents empirical findings about relationships between student behaviours in a learning management system (LMS) and the learning outcomes of students, and further explores these findings using process modelling techniques. Design/methodology/approach The paper describes a two-year study in a Chilean university, using big data from a LMS and from the central university database of student results and demographics. Descriptive statistics of LMS use in different years presents an overall picture of student use of the system. Process mining is described as an event-centred approach to give a deeper level of understanding of these findings. Findings The study found evidence to support the idea that instructors do not strongly influence student use of an LMS. It replicates existing studies to show that higher-performing students use an LMS differently from the lower-performing students. It shows the value of combining variable- and event-centred approaches to learning analytics. Research limitations/implications The study is limited by its institutional context, its two-year time frame and by its exploratory mode of investigation to create a case study. Practical implications The paper is useful for institutions in developing a methodology for using big data from a LMS to make use of event-centred approaches. Originality/value The paper is valuable in replicating and extending recent studies using event-centred approaches to analysis of learning data. The study here is on a larger scale than the existing studies (using a university-wide data set), in a novel context (Latin America), that provides a clear description for how and why the methodology should inform institutional approaches.

Analysis of engagement metrics vs student attainment using multiple sources across physical and virtual spaces: A case study.

2020 ◽  
Author(s):  
Caudia Wascher ◽  
Isobel Gowers ◽  
Matt East
Keyword(s):  
Higher Education ◽  
Learning Environment ◽  
Learning Analytics ◽  
Higher Education Institutions ◽  
Virtual Learning ◽  
Individual Learning ◽  
Virtual Learning Environment ◽  
Multiple Sources ◽  
Student Behaviour

Learning analytics, referring to the collection and analysis of data regarding the progress of learners, allows higher education institutions and individual academics to make data driven decisions regarding their teaching approaches and support they are providing. Further, they provide students with an opportunity to take control of their own learning, as they are gaining a better understanding of their own performance and can make informed decisions about their own learning progress. In early 2020 a global pandemic forced higher education institutions worldwide to quickly move teaching online. We argue that under these circumstances, detailed learning analytics provide a unique opportunity to understand student behaviour and support individual learning. We present a case study analysing engagement metrics and their relationship to student attainment in four courses in the area of behavioural biology, over a time period of two years pre-pandemic. Multiple sources of student engagement in the physical (attendance at lectures) and virtual space (access and engagement with online learning resources) were used. Our results show that grades of students were significantly affected by type of assignment, with grades being lower in exams compared to other types of assignment. Grades were not significantly affected by level of studies, gender and country of origin (UK versus non-UK). With regards to engagement metrics, grades significantly increased with percentage of attendance in class, percentage of resources accessed on Canvas and library access. Students accessed lecture notes longer compared to other resources. Physical attendance in class over all courses and levels of studies averaged at 55 %. Online, students accessed on average only 32 % of resources provided in the virtual learning environment. Students accessed the majority of the courses in the same week when materials were discussed in class compared to the weeks before and after. Our results show that both engagement with materials in the virtual learning environment and attendance in class are positively correlated with student achievement. We cannot make any inferences about the causality of this effect and it is likely that better students in general are more engaged. Our project provides detailed in-depth insight into student behaviour and reveals that students overall do not engage with all materials provided, resulting in an incomplete learning experience. We suggest that detailed data on engagement of students with individual resources can be used to better understand and shape individual learning experiences of students.

Learning Analytics: The Emperor’s New Clothes?

2017 ◽  
Vol 3 (1) ◽  
pp. 76
Author(s):  
Sue Milward
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Student Retention ◽  
Student Learning ◽  
Learning Analytics

Learning Analytics is promising to deliver the power of big data to Higher Education. By extracting meaning from the myriad of data held against a student, Learning Analytics promises to improve student retention and attainment. However, there are challenges to be overcome before the reality can live up to the promises.  

Learning Analytics for E-Book-Based Educational Big Data in Higher Education

2017 ◽  
pp. 327-350 ◽  
Author(s):  
Hiroaki Ogata ◽  
Misato Oi ◽  
Kousuke Mohri ◽  
Fumiya Okubo ◽  
Atsushi Shimada ◽  
...  
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Learning Analytics

scholarly journals A Generalizable Framework for Multi-Scale Auditing of Digital Learning Provision in Higher Education

2018 ◽  
Vol 22 (2) ◽  
Author(s):  
Samuel Robert Peter-James Ross ◽  
Veronica Elizabeth Volz ◽  
Matthew K Lancaster ◽  
Aysha Divan
Keyword(s):  
Higher Education ◽  
Learning Analytics ◽  
Digital Learning ◽  
Higher Education Administrators ◽  
Multi Scale ◽  
Education Administrators ◽  
Department Faculty ◽  
Digital Learning Resources ◽  
Individual Programme

It is increasingly important that higher education institutions can audit and evaluate the scope and efficacy of their digital learning resources across various scales. To-date there has been little effort to address this need for a validated, appropriate and simple to execute method that will facilitate such an audit; whether it be at the scale of an individual programme, department, faculty or institution. The data are of increasing value to ensure institutions maintain progress and equity in the student experience as well as for deployment and interpretation of learning analytics. This study presents a generalizable framework for auditing digital learning provision in higher education curricula. The framework is contextualized using a case study in which the audit is conducted across a single faculty in a research-intensive U.K. university. This work provides academics and higher education administrators with key principles and considerations as well as example aims and outcomes.

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